<xarray.DatasetView> Size: 0B
Dimensions: ()
Data variables:
*empty*xarray.DataTree
<xarray.DatasetView> Size: 24MB Dimensions: (chain: 4, draw: 2000, party_id_dim: 2, party_id:age_dim: 3, __obs__: 373) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 16kB 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999 * party_id_dim (party_id_dim) <U11 88B 'independent' 'republican' * party_id:age_dim (party_id:age_dim) <U11 132B 'democrat' ... 'republican' * __obs__ (__obs__) int64 3kB 0 1 2 3 4 5 ... 368 369 370 371 372 Data variables: Intercept (chain, draw) float64 64kB 1.77 0.5591 ... 1.741 2.553 party_id (chain, draw, party_id_dim) float64 128kB 0.08426 ... -... party_id:age (chain, draw, party_id:age_dim) float64 192kB 0.01229 .... p (chain, draw, __obs__) float64 24MB 0.9211 ... 0.5189 Attributes: created_at: 2025-04-03T07:02:38.257111+00:00 arviz_version: 0.21.0 inference_library: pymc inference_library_version: 5.21.1 sampling_time: 7.0342934131622314 tuning_steps: 1000 modeling_interface: bambi modeling_interface_version: 0.12.1.dev79+g649a304a.d20250403posterior- chain: 4
- draw: 2000
- party_id_dim: 2
- party_id:age_dim: 3
- __obs__: 373
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 ... 1996 1997 1998 1999
array([ 0, 1, 2, ..., 1997, 1998, 1999], shape=(2000,))
- party_id_dim(party_id_dim)<U11'independent' 'republican'
array(['independent', 'republican'], dtype='<U11')
- party_id:age_dim(party_id:age_dim)<U11'democrat' ... 'republican'
array(['democrat', 'independent', 'republican'], dtype='<U11')
- __obs__(__obs__)int640 1 2 3 4 5 ... 368 369 370 371 372
array([ 0, 1, 2, ..., 370, 371, 372], shape=(373,))
- Intercept(chain, draw)float641.77 0.5591 0.7926 ... 1.741 2.553
array([[1.76983427, 0.55910868, 0.7926086 , ..., 1.72090995, 1.8421321 , 1.25050209], [1.64165941, 1.16058701, 1.47489376, ..., 1.42243339, 1.02016786, 0.34693475], [1.61679813, 2.04075067, 3.2408817 , ..., 1.57871759, 1.71889269, 1.17829081], [0.90652157, 2.76189849, 2.39000988, ..., 1.98605176, 1.74077432, 2.55253697]], shape=(4, 2000)) - party_id(chain, draw, party_id_dim)float640.08426 -1.538 ... -1.133 -1.033
array([[[ 0.08426441, -1.53771251], [ 0.71636811, 0.27587536], [ 0.3223159 , -0.37606385], ..., [-0.79325747, 0.12728313], [-1.07700936, -0.37948107], [-0.39459464, 0.20523853]], [[-0.7849811 , -0.07110305], [-0.41991819, -1.05531664], [ 0.46164391, 1.0771114 ], ..., [ 0.22300449, -0.06933184], [ 0.28946187, -0.36897372], [ 1.66661367, -1.52669661]], [[-0.40973455, -0.86062057], [-0.71869175, 0.21182576], [-1.75499248, -1.40365043], ..., [-0.33516289, -1.78311094], [-0.22135259, -0.04277376], [-0.57954108, -0.64667506]], [[-0.32628203, -1.01638903], [-1.50269894, -0.66358843], [-1.98072789, -1.88001738], ..., [-0.34314376, -1.64882706], [-0.77958538, -0.88177686], [-1.13315575, -1.03328797]]], shape=(4, 2000, 2)) - party_id:age(chain, draw, party_id:age_dim)float640.01229 -0.04898 ... -0.09971
array([[[ 0.01228726, -0.04898431, -0.06505162], [ 0.03452198, -0.02545731, -0.08971955], [ 0.0327688 , -0.0321916 , -0.10258642], ..., [ 0.00892146, -0.02649995, -0.10488666], [ 0.00478685, -0.02410694, -0.0953596 ], [ 0.01272674, -0.01913287, -0.08882076]], [[ 0.01522983, -0.02497561, -0.09075746], [ 0.01678022, -0.02132958, -0.06323353], [ 0.0156651 , -0.04562215, -0.11605304], ..., [ 0.01938449, -0.04157974, -0.10776656], [ 0.03107276, -0.03960364, -0.06463479], [ 0.04583221, -0.04252883, -0.04146132]], [[ 0.00919431, -0.03107038, -0.07199775], [-0.00109037, -0.03182908, -0.12368915], [-0.01910361, -0.03482603, -0.1215257 ], ..., [ 0.01205641, -0.02909065, -0.06944021], [ 0.00955852, -0.0306304 , -0.0869839 ], [ 0.02868762, -0.01644882, -0.07834531]], [[ 0.02530963, -0.02026135, -0.04767936], [-0.00505738, -0.03206405, -0.09621422], [-0.00068329, -0.01927701, -0.08768054], ..., [ 0.00681589, -0.03156575, -0.07157974], [ 0.0118676 , -0.02785339, -0.09113042], [-0.00419307, -0.03445131, -0.09970661]]], shape=(4, 2000, 3)) - p(chain, draw, __obs__)float640.9211 0.01805 ... 0.916 0.5189
array([[[0.92113875, 0.01805286, 0.94007212, ..., 0.92799018, 0.90456746, 0.48593138], [0.92360303, 0.00671425, 0.96513776, ..., 0.94095053, 0.87050889, 0.57019372], [0.93262075, 0.00192371, 0.96814269, ..., 0.9473405 , 0.88801183, 0.46492079], ..., [0.9020764 , 0.00689911, 0.91942993, ..., 0.90820258, 0.88783818, 0.47356327], [0.89189021, 0.00869975, 0.90247922, ..., 0.89552764, 0.88378987, 0.45634939], [0.87687881, 0.01315633, 0.90624643, ..., 0.88745589, 0.85155443, 0.52740393]], [[0.92375746, 0.01301338, 0.94583565, ..., 0.93190763, 0.90340315, 0.47069098], [0.89093035, 0.01789971, 0.92435221, ..., 0.90330699, 0.85996698, 0.47721957], [0.91310415, 0.00674998, 0.93866466, ..., 0.92254653, 0.88951856, 0.53923764], ... [0.9049844 , 0.00885387, 0.92711741, ..., 0.91296029, 0.88583821, 0.52722786], [0.9050096 , 0.01838193, 0.92298225, ..., 0.91138286, 0.89009138, 0.57516462], [0.94184875, 0.01034524, 0.96991845, ..., 0.95321591, 0.90863723, 0.48931303]], [[0.910834 , 0.03882747, 0.94937181, ..., 0.9259677 , 0.86916513, 0.44770366], [0.92263402, 0.01543421, 0.91351339, ..., 0.9196962 , 0.92855229, 0.50217536], [0.91307288, 0.00554531, 0.91176243, ..., 0.91263803, 0.91399043, 0.41519714], ..., [0.91433413, 0.01318428, 0.92630871, ..., 0.91850972, 0.90481184, 0.60152989], [0.9172351 , 0.00627754, 0.93644482, ..., 0.92416289, 0.90057328, 0.46876743], [0.91033442, 0.00695247, 0.90177385, ..., 0.90755837, 0.9159852 , 0.518936 ]]], shape=(4, 2000, 373))
- created_at :
- 2025-04-03T07:02:38.257111+00:00
- arviz_version :
- 0.21.0
- inference_library :
- pymc
- inference_library_version :
- 5.21.1
- sampling_time :
- 7.0342934131622314
- tuning_steps :
- 1000
- modeling_interface :
- bambi
- modeling_interface_version :
- 0.12.1.dev79+g649a304a.d20250403
<xarray.DatasetView> Size: 24MB Dimensions: (chain: 4, draw: 2000, __obs__: 373) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999 * __obs__ (__obs__) int64 3kB 0 1 2 3 4 5 6 7 ... 366 367 368 369 370 371 372 Data variables: vote (chain, draw, __obs__) int64 24MB 1 0 1 0 0 1 1 1 ... 1 1 1 0 1 1 0 Attributes: modeling_interface: bambi modeling_interface_version: 0.12.1.dev79+g649a304a.d20250403posterior_predictive- chain: 4
- draw: 2000
- __obs__: 373
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 ... 1996 1997 1998 1999
array([ 0, 1, 2, ..., 1997, 1998, 1999], shape=(2000,))
- __obs__(__obs__)int640 1 2 3 4 5 ... 368 369 370 371 372
array([ 0, 1, 2, ..., 370, 371, 372], shape=(373,))
- vote(chain, draw, __obs__)int641 0 1 0 0 1 1 1 ... 0 1 1 1 0 1 1 0
array([[[1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 0], [1, 0, 1, ..., 1, 1, 1], ..., [1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 0, 0, 0]], [[1, 0, 1, ..., 1, 1, 1], [0, 0, 1, ..., 0, 1, 0], [1, 0, 1, ..., 1, 0, 0], ..., [0, 0, 1, ..., 1, 0, 0], [1, 0, 1, ..., 1, 1, 0], [1, 0, 1, ..., 1, 1, 0]], [[0, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 1], ..., [1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 0]], [[1, 0, 1, ..., 1, 1, 0], [0, 0, 0, ..., 1, 1, 1], [1, 0, 1, ..., 1, 1, 0], ..., [0, 0, 1, ..., 1, 1, 1], [1, 0, 1, ..., 1, 0, 0], [1, 0, 1, ..., 1, 1, 0]]], shape=(4, 2000, 373))
- modeling_interface :
- bambi
- modeling_interface_version :
- 0.12.1.dev79+g649a304a.d20250403
<xarray.DatasetView> Size: 24MB Dimensions: (chain: 4, draw: 2000, __obs__: 373) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999 * __obs__ (__obs__) int64 3kB 0 1 2 3 4 5 6 7 ... 366 367 368 369 370 371 372 Data variables: vote (chain, draw, __obs__) float64 24MB -0.08214 -0.01822 ... -0.656 Attributes: created_at: 2025-04-03T07:02:38.776345+00:00 arviz_version: 0.21.0 inference_library: pymc inference_library_version: 5.21.1 modeling_interface: bambi modeling_interface_version: 0.12.1.dev79+g649a304a.d20250403log_likelihood- chain: 4
- draw: 2000
- __obs__: 373
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 ... 1996 1997 1998 1999
array([ 0, 1, 2, ..., 1997, 1998, 1999], shape=(2000,))
- __obs__(__obs__)int640 1 2 3 4 5 ... 368 369 370 371 372
array([ 0, 1, 2, ..., 370, 371, 372], shape=(373,))
- vote(chain, draw, __obs__)float64-0.08214 -0.01822 ... -0.656
array([[[-0.08214461, -0.0182178 , -0.06179868, ..., -0.07473412, -0.10029839, -0.72168785], [-0.07947292, -0.00673689, -0.03548443, ..., -0.06086471, -0.13867731, -0.56177911], [-0.06975665, -0.00192557, -0.03237579, ..., -0.0540967 , -0.11877021, -0.76588824], ..., [-0.10305606, -0.00692302, -0.08400144, ..., -0.09628782, -0.11896578, -0.74746974], [-0.11441224, -0.00873782, -0.10260961, ..., -0.11034219, -0.12353595, -0.78449656], [-0.13138648, -0.01324364, -0.09844402, ..., -0.11939646, -0.16069186, -0.63978855]], [[-0.07930573, -0.0130988 , -0.05568646, ..., -0.07052158, -0.10158637, -0.7535535 ], [-0.11548903, -0.01806185, -0.0786621 , ..., -0.10169282, -0.15086128, -0.73977858], [-0.09090533, -0.00677287, -0.06329699, ..., -0.08061746, -0.1170749 , -0.61759891], ... [-0.09983757, -0.0088933 , -0.07567506, ..., -0.0910629 , -0.12122096, -0.64012246], [-0.09980973, -0.01855297, -0.08014528, ..., -0.0927922 , -0.11643114, -0.55309898], [-0.05991058, -0.01039913, -0.03054328, ..., -0.04791385, -0.09580936, -0.71475286]], [[-0.09339461, -0.03960136, -0.05195476, ..., -0.07691592, -0.14022215, -0.80362375], [-0.08052264, -0.01555456, -0.09045724, ..., -0.08371188, -0.07412858, -0.68880589], [-0.09093958, -0.00556074, -0.09237581, ..., -0.09141594, -0.08993518, -0.87900183], ..., [-0.0895592 , -0.01327196, -0.07654772, ..., -0.08500279, -0.10002827, -0.50827906], [-0.08639146, -0.00629733, -0.06566468, ..., -0.07886694, -0.10472374, -0.75764852], [-0.09394325, -0.00697676, -0.10339151, ..., -0.0969974 , -0.08775507, -0.65597472]]], shape=(4, 2000, 373))
- created_at :
- 2025-04-03T07:02:38.776345+00:00
- arviz_version :
- 0.21.0
- inference_library :
- pymc
- inference_library_version :
- 5.21.1
- modeling_interface :
- bambi
- modeling_interface_version :
- 0.12.1.dev79+g649a304a.d20250403
<xarray.DatasetView> Size: 992kB Dimensions: (chain: 4, draw: 2000) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999 Data variables: (12/17) acceptance_rate (chain, draw) float64 64kB 1.0 0.9295 ... 0.9713 1.0 step_size_bar (chain, draw) float64 64kB 0.309 0.309 ... 0.3213 lp (chain, draw) float64 64kB -148.7 -148.7 ... -148.1 step_size (chain, draw) float64 64kB 0.2691 0.2691 ... 0.3602 process_time_diff (chain, draw) float64 64kB 0.001115 ... 0.0009314 perf_counter_start (chain, draw) float64 64kB 5.431e+03 ... 5.434e+03 ... ... largest_eigval (chain, draw) float64 64kB nan nan nan ... nan nan energy (chain, draw) float64 64kB 153.5 152.0 ... 148.8 smallest_eigval (chain, draw) float64 64kB nan nan nan ... nan nan index_in_trajectory (chain, draw) int64 64kB 14 7 -3 -3 -9 ... 12 -1 8 6 diverging (chain, draw) bool 8kB False False ... False False n_steps (chain, draw) float64 64kB 15.0 15.0 ... 15.0 7.0 Attributes: created_at: 2025-04-03T07:02:38.284188+00:00 arviz_version: 0.21.0 inference_library: pymc inference_library_version: 5.21.1 sampling_time: 7.0342934131622314 tuning_steps: 1000 modeling_interface: bambi modeling_interface_version: 0.12.1.dev79+g649a304a.d20250403sample_stats- chain: 4
- draw: 2000
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 ... 1996 1997 1998 1999
array([ 0, 1, 2, ..., 1997, 1998, 1999], shape=(2000,))
- acceptance_rate(chain, draw)float641.0 0.9295 0.821 ... 0.9713 1.0
array([[1. , 0.92945233, 0.82097052, ..., 0.97373513, 0.89515331, 0.83384143], [0.81266863, 0.99375132, 0.90677111, ..., 0.96441623, 0.74338548, 0.89218632], [1. , 0.91307546, 0.97539869, ..., 0.91584965, 0.9174156 , 0.99738606], [0.71324222, 0.86114911, 0.86437499, ..., 0.76712117, 0.97133759, 1. ]], shape=(4, 2000)) - step_size_bar(chain, draw)float640.309 0.309 0.309 ... 0.3213 0.3213
array([[0.30899056, 0.30899056, 0.30899056, ..., 0.30899056, 0.30899056, 0.30899056], [0.31853086, 0.31853086, 0.31853086, ..., 0.31853086, 0.31853086, 0.31853086], [0.30363053, 0.30363053, 0.30363053, ..., 0.30363053, 0.30363053, 0.30363053], [0.32127962, 0.32127962, 0.32127962, ..., 0.32127962, 0.32127962, 0.32127962]], shape=(4, 2000)) - lp(chain, draw)float64-148.7 -148.7 ... -147.1 -148.1
array([[-148.70530601, -148.67853387, -149.96479263, ..., -147.31284025, -147.72473766, -148.5728922 ], [-147.22444427, -147.15033722, -149.315142 , ..., -147.56555339, -149.25325045, -151.62833547], [-146.43879915, -148.53099633, -152.20735005, ..., -147.5762389 , -147.86047668, -147.81036697], [-147.90244437, -150.42205481, -152.13107368, ..., -148.34711308, -147.08277073, -148.10005814]], shape=(4, 2000)) - step_size(chain, draw)float640.2691 0.2691 ... 0.3602 0.3602
array([[0.26911638, 0.26911638, 0.26911638, ..., 0.26911638, 0.26911638, 0.26911638], [0.38002602, 0.38002602, 0.38002602, ..., 0.38002602, 0.38002602, 0.38002602], [0.22276185, 0.22276185, 0.22276185, ..., 0.22276185, 0.22276185, 0.22276185], [0.3601677 , 0.3601677 , 0.3601677 , ..., 0.3601677 , 0.3601677 , 0.3601677 ]], shape=(4, 2000)) - process_time_diff(chain, draw)float640.001115 0.001084 ... 0.0009314
array([[0.00111524, 0.00108438, 0.00133201, ..., 0.00211049, 0.00066776, 0.00122466], [0.00212674, 0.00187272, 0.00182009, ..., 0.00089411, 0.00177037, 0.00132954], [0.0005711 , 0.00154027, 0.00075049, ..., 0.0019136 , 0.0017291 , 0.00189396], [0.00102347, 0.00192708, 0.00197426, ..., 0.00206303, 0.00191825, 0.00093145]], shape=(4, 2000)) - perf_counter_start(chain, draw)float645.431e+03 5.431e+03 ... 5.434e+03
array([[5430.77516105, 5430.77636862, 5430.77753933, ..., 5434.25705354, 5434.25932815, 5434.26010746], [5430.68575543, 5430.6880411 , 5430.69006339, ..., 5434.08393049, 5434.08496077, 5434.08689181], [5430.66084131, 5430.66150646, 5430.66318983, ..., 5433.99005734, 5433.99213483, 5433.99402084], [5430.45621336, 5430.45739895, 5430.46974699, ..., 5433.81919438, 5433.82140507, 5433.82345255]], shape=(4, 2000)) - tree_depth(chain, draw)int644 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 3
array([[4, 4, 4, ..., 4, 3, 4], [4, 4, 4, ..., 3, 4, 4], [3, 4, 3, ..., 4, 4, 4], [3, 4, 4, ..., 4, 4, 3]], shape=(4, 2000)) - max_energy_error(chain, draw)float64-0.9289 -0.4759 ... -0.4903 -0.1082
array([[-0.9289275 , -0.47589878, 0.61240121, ..., 0.08846212, 0.24269085, 0.39117628], [ 0.4655421 , -0.13629235, 0.26203839, ..., -0.19592508, 0.8239426 , -0.8440365 ], [-0.33930122, 0.22513729, 0.07525231, ..., -1.12910439, 0.33982075, -0.25807931], [ 0.54332467, 0.37496515, 0.419284 , ..., 0.67271785, -0.49034375, -0.10821431]], shape=(4, 2000)) - reached_max_treedepth(chain, draw)boolFalse False False ... False False
array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], shape=(4, 2000)) - perf_counter_diff(chain, draw)float640.001115 0.001084 ... 0.0009312
array([[0.00111502, 0.00108413, 0.00133109, ..., 0.0021672 , 0.00066756, 0.00139498], [0.00212716, 0.00187175, 0.00181998, ..., 0.00089394, 0.00176659, 0.00132921], [0.00057089, 0.00153972, 0.00075011, ..., 0.00191357, 0.00172859, 0.00187008], [0.00102366, 0.00192706, 0.00197396, ..., 0.00206292, 0.00191814, 0.00093125]], shape=(4, 2000)) - energy_error(chain, draw)float64-0.4452 0.02966 ... -0.4109 -0.1
array([[-0.44520067, 0.02966244, -0.12548576, ..., 0.0181993 , 0.02839163, 0.23821905], [ 0.0069581 , -0.10480206, 0.12775947, ..., -0.05260767, 0.73829172, -0.56331777], [-0.33930122, 0.00754553, 0.07525231, ..., -1.04677254, 0.123882 , -0.1318708 ], [ 0.21252682, 0.0954863 , 0.25080288, ..., 0.38007745, -0.41091127, -0.09999869]], shape=(4, 2000)) - largest_eigval(chain, draw)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], shape=(4, 2000)) - energy(chain, draw)float64153.5 152.0 153.3 ... 150.1 148.8
array([[153.46452834, 152.006794 , 153.3190873 , ..., 149.00223703, 148.83159172, 150.22821793], [150.17395029, 148.13837035, 149.92758689, ..., 149.53176868, 151.29302051, 156.74973241], [149.74410498, 149.79022524, 153.43777301, ..., 150.7003475 , 149.2769411 , 150.1666214 ], [148.99543095, 153.88356664, 154.79835881, ..., 151.4898612 , 150.11010962, 148.82840215]], shape=(4, 2000)) - smallest_eigval(chain, draw)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]], shape=(4, 2000)) - index_in_trajectory(chain, draw)int6414 7 -3 -3 -9 -7 ... 8 12 12 -1 8 6
array([[14, 7, -3, ..., 6, -5, -6], [ 9, 6, 8, ..., 4, -9, -3], [ 6, -7, 4, ..., -7, 8, 3], [-3, 5, -5, ..., -1, 8, 6]], shape=(4, 2000)) - diverging(chain, draw)boolFalse False False ... False False
array([[False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False], [False, False, False, ..., False, False, False]], shape=(4, 2000)) - n_steps(chain, draw)float6415.0 15.0 15.0 ... 15.0 15.0 7.0
array([[15., 15., 15., ..., 15., 7., 15.], [15., 15., 15., ..., 7., 15., 11.], [ 7., 15., 7., ..., 15., 15., 15.], [ 7., 15., 15., ..., 15., 15., 7.]], shape=(4, 2000))
- created_at :
- 2025-04-03T07:02:38.284188+00:00
- arviz_version :
- 0.21.0
- inference_library :
- pymc
- inference_library_version :
- 5.21.1
- sampling_time :
- 7.0342934131622314
- tuning_steps :
- 1000
- modeling_interface :
- bambi
- modeling_interface_version :
- 0.12.1.dev79+g649a304a.d20250403
<xarray.DatasetView> Size: 6kB Dimensions: (__obs__: 373) Coordinates: * __obs__ (__obs__) int64 3kB 0 1 2 3 4 5 6 7 ... 366 367 368 369 370 371 372 Data variables: vote (__obs__) int64 3kB 1 0 1 0 0 0 1 1 1 1 1 ... 1 1 0 1 1 1 1 0 1 1 1 Attributes: created_at: 2025-04-03T07:02:38.288944+00:00 arviz_version: 0.21.0 inference_library: pymc inference_library_version: 5.21.1 modeling_interface: bambi modeling_interface_version: 0.12.1.dev79+g649a304a.d20250403observed_data- __obs__: 373
- __obs__(__obs__)int640 1 2 3 4 5 ... 368 369 370 371 372
array([ 0, 1, 2, ..., 370, 371, 372], shape=(373,))
- vote(__obs__)int641 0 1 0 0 0 1 1 ... 1 1 1 1 0 1 1 1
array([1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1])
- created_at :
- 2025-04-03T07:02:38.288944+00:00
- arviz_version :
- 0.21.0
- inference_library :
- pymc
- inference_library_version :
- 5.21.1
- modeling_interface :
- bambi
- modeling_interface_version :
- 0.12.1.dev79+g649a304a.d20250403